dc.contributor.author |
Cronje, J
|
|
dc.date.accessioned |
2011-12-12T12:50:44Z |
|
dc.date.available |
2011-12-12T12:50:44Z |
|
dc.date.issued |
2011-11 |
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dc.identifier.citation |
Cronje, J. 2011. BFROST: binary features from robust orientation segment tests accelerated on the GPU. 22nd Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Emerald Casino and Resort, Vanderbijlpark, South Africa, 22-25 November 2011 |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10204/5387
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|
dc.description |
22nd Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Emerald Casino and Resort, Vanderbijlpark, South Africa, 22-25 November 2011 |
en_US |
dc.description.abstract |
A fast local image feature detector and descriptor that is implementable on the GPU is proposed. This method is the first GPU implementation of the popular FAST detector. A simple but novel method of feature orientation estimation which can be calculated in constant time is proposed. The robustness and reliability of our orientation estimation is validated against rotation invariant descriptors such as SIFT and SURF. Furthermore, a binary feature descriptor is proposed which is robust to noise, scalable, rotation invariant, fast to compute in parallel and maintains low memory consumption. The proposed method demonstrates good robustness and very fast computation times, making it usable in real-time applications. |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.ispartofseries |
Workflow request;7659 |
|
dc.subject |
Computer vision |
en_US |
dc.subject |
Feature detection |
en_US |
dc.subject |
Feature extraction |
en_US |
dc.subject |
FAST detector |
en_US |
dc.subject |
BFROST |
en_US |
dc.subject |
Pattern recognition association |
en_US |
dc.subject |
PRASA 2011 |
en_US |
dc.title |
BFROST: binary features from robust orientation segment tests accelerated on the GPU |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Cronje, J. (2011). BFROST: binary features from robust orientation segment tests accelerated on the GPU. http://hdl.handle.net/10204/5387 |
en_ZA |
dc.identifier.chicagocitation |
Cronje, J. "BFROST: binary features from robust orientation segment tests accelerated on the GPU." (2011): http://hdl.handle.net/10204/5387 |
en_ZA |
dc.identifier.vancouvercitation |
Cronje J, BFROST: binary features from robust orientation segment tests accelerated on the GPU; 2011. http://hdl.handle.net/10204/5387 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Cronje, J
AB - A fast local image feature detector and descriptor that is implementable on the GPU is proposed. This method is the first GPU implementation of the popular FAST detector. A simple but novel method of feature orientation estimation which can be calculated in constant time is proposed. The robustness and reliability of our orientation estimation is validated against rotation invariant descriptors such as SIFT and SURF. Furthermore, a binary feature descriptor is proposed which is robust to noise, scalable, rotation invariant, fast to compute in parallel and maintains low memory consumption. The proposed method demonstrates good robustness and very fast computation times, making it usable in real-time applications.
DA - 2011-11
DB - ResearchSpace
DP - CSIR
KW - Computer vision
KW - Feature detection
KW - Feature extraction
KW - FAST detector
KW - BFROST
KW - Pattern recognition association
KW - PRASA 2011
LK - https://researchspace.csir.co.za
PY - 2011
T1 - BFROST: binary features from robust orientation segment tests accelerated on the GPU
TI - BFROST: binary features from robust orientation segment tests accelerated on the GPU
UR - http://hdl.handle.net/10204/5387
ER -
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en_ZA |